/supervised-learning-with-scikit-learn

This was done as part of datacamp course : https://campus.datacamp.com/courses/supervised-learning-with-scikit-learn

Primary LanguageJupyter Notebook

supervised-learning-with-scikit-learn

This was done as part of datacamp course : https://campus.datacamp.com/courses/supervised-learning-with-scikit-learn

This course gave me a solid confidence to apply Machine Learning to datasets. All the .ipynb files uploaded in my git are 'ready to run'. I am uploading these for my reference. These exercises may not make much sense to you unless you take the above mentioned datacamp course.

Modules are in following order

1. classification

  • Supervised Learning, Exploratory Data Analysis, k- Nearest Neighbors, Measuring Model Performance, Train Test Split, Fit Predict Accuracy, Overfitting and underfitting

2. regression

  • The basics of Linear Regression, Cross - Validation, K- fold CV comparison, Regularized Regression, Lasso, Ridge

3. fine_tuning_your_model

  • Logistic Regression and LOC curve, Precision-Recall Curve, Area Under the ROC curve, AUC Computation, Hyperparameter Tuning, GridSearchCV, RandomizedSearchCV, Hold-out set

4. preprocessing_and_pipeline

  • Preprocessing data, Creating Dummy Variables, Regression with Categorical features, Handling missing data, Imputing missing data in ML pipeline, Centering and Scaling, Pipeline for Classification, Pipeline for Regression